Chapter 6 Functional differences

load("data/data.Rdata")

6.1 Data preparation

# Aggregate bundle-level GIFTs into the compound level
GIFTs_elements <- to.elements(genome_gifts, GIFT_db)
GIFTs_elements_filtered <- GIFTs_elements[rownames(GIFTs_elements) %in% genome_counts$genome, ]
GIFTs_elements_filtered <- as.data.frame(GIFTs_elements_filtered) %>%
  select_if(~ !is.numeric(.) || sum(.) != 0)

elements <- GIFTs_elements_filtered %>%
  as.data.frame()

# Aggregate element-level GIFTs into the function level
GIFTs_functions <- to.functions(GIFTs_elements_filtered, GIFT_db)
functions <- GIFTs_functions %>%
  as.data.frame()

# Aggregate function-level GIFTs into overall Biosynthesis, Degradation and Structural GIFTs
GIFTs_domains <- to.domains(GIFTs_functions, GIFT_db)
domains <- GIFTs_domains %>%
  as.data.frame()

# Get community-weighed average GIFTs per sample
GIFTs_elements_community <- to.community(GIFTs_elements_filtered, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db)
GIFTs_functions_community <- to.community(GIFTs_functions, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db)
GIFTs_domains_community <- to.community(GIFTs_domains, genome_counts_filt %>% column_to_rownames(., "genome") %>% tss(), GIFT_db)

uniqueGIFT_db<- unique(GIFT_db[c(2,4,5,6)]) %>% unite("Function",Function:Element, sep= "_", remove=FALSE)

6.2 Genomes GIFT profiles

GIFTs_elements %>%
  as_tibble(., rownames = "MAG") %>%
  reshape2::melt() %>%
  rename(Code_element = variable, GIFT = value) %>%
  inner_join(GIFT_db,by="Code_element") %>%
  ggplot(., aes(x=Code_element, y=MAG, fill=GIFT, group=Code_function))+
    geom_tile()+
    scale_y_discrete(guide = guide_axis(check.overlap = TRUE))+
    scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
    scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
    facet_grid(. ~ Code_function, scales = "free", space = "free")+
    theme_grey(base_size=8)+
    theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),strip.text.x = element_text(angle = 90))

6.3 Function level

GIFTs_functions_community %>%
    as.data.frame() %>%
    rownames_to_column(var="sample") %>%
    pivot_longer(!sample,names_to="trait",values_to="gift") %>%
    left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
    ggplot(aes(x=trait,y=time_point,fill=gift)) +
        geom_tile(colour="white", size=0.2)+
        scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
        facet_grid(type ~ ., scales="free",space="free")

6.4 Element level

GIFTs_elements_community_merged<-GIFTs_elements_community %>%
    as.data.frame() %>%
    rownames_to_column(var="sample") %>%
    pivot_longer(!sample,names_to="trait",values_to="gift") %>%
    left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
    mutate(functionid = substr(trait, 1, 3)) %>%
    mutate(trait = case_when(
      trait %in% GIFT_db$Code_element ~ GIFT_db$Element[match(trait, GIFT_db$Code_element)],
      TRUE ~ trait
    )) %>%
    mutate(functionid = case_when(
      functionid %in% GIFT_db$Code_function ~ GIFT_db$Function[match(functionid, GIFT_db$Code_function)],
      TRUE ~ functionid
    )) %>%
    mutate(trait=factor(trait,levels=unique(GIFT_db$Element))) %>%
    mutate(functionid=factor(functionid,levels=unique(GIFT_db$Function)))

# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
  
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
        geom_tile(colour="white", linewidth=0.2)+
        scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
        facet_grid(functionid ~ type, scales="free",space="free") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Time_point",fill="GIFT")+
  scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))

6.5 Comparison of samples from the 0 Time_point (0_Wild)

6.5.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.328 0.0232
2 Podarcis_muralis  0.346 0.0194

6.5.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  select(c(1:21, 23)) %>%
  pivot_longer(-c(sample,species),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db$Code_function ~ GIFT_db$Function[match(trait, GIFT_db$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db$Function))) %>%
  ggplot(aes(x=value, y=species, group=species, fill=species, color=species)) +
    geom_boxplot() +
    scale_color_manual(name="species",
          breaks=c("Podarcis_liolepis","Podarcis_muralis"),
          labels=c("Podarcis liolepis","Podarcis muralis"),
          values=c("#e5bd5b","#6b7398")) +
      scale_fill_manual(name="species",
          breaks=c("Podarcis_liolepis","Podarcis_muralis"),
          labels=c("Podarcis liolepis","Podarcis muralis"),
          values=c("#e5bd5b50","#6b739850")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

6.5.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.372 0.0316
2 Podarcis_muralis  0.390 0.0208

6.5.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  group_by(species) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 2 × 3
  species             MCI     sd
  <chr>             <dbl>  <dbl>
1 Podarcis_liolepis 0.312 0.0311
2 Podarcis_muralis  0.345 0.0233
sample_metadata_wild <- sample_metadata%>% 
  filter(time_point == "0_Wild")

element_gift_wild <- GIFTs_elements_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_wild[c(1,3)], by="Tube_code")
# Find numeric columns
numeric_cols <- sapply(element_gift_wild, is.numeric)

# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_wild[, numeric_cols])

# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]

# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_wild[, !numeric_cols | colnames(element_gift_wild) %in% nonzero_numeric_cols]
significant_elements_wild <- filtered_data %>%
  pivot_longer(-c(Tube_code,species), names_to = "trait", values_to = "value") %>%
  group_by(trait) %>%
  summarise(p_value = wilcox.test(value ~ species, exact=FALSE)$p.value) %>%
  mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
  filter(p_adjust < 0.05)  %>%
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))

element_gift_t <- element_gift_wild  %>% 
  dplyr::select(-c(species))  %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "trait")

element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_wild$trait) %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Tube_code")%>% 
  left_join(., sample_metadata_wild[c(1,3)], by = join_by(Tube_code == Tube_code))

element_gift_filt %>%
  dplyr::select(-Tube_code)%>%
  group_by(species)  %>%
  summarise(across(everything(), mean))%>%
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))

element_gift_names <- element_gift_filt%>%
  dplyr::select(-species)%>%
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
  dplyr::select(-Elements)%>%
  dplyr::select(Function, everything())%>%
  t()%>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Tube_code")%>% 
  left_join(., sample_metadata_wild[c(1,3)], by = join_by(Tube_code == Tube_code))
colNames <- names(element_gift_names)[2:36] #always check names(element_gift_names) first to know were your traits finish
for(i in colNames){
  plt <- ggplot(element_gift_names, aes(x=species, y=.data[[i]], color = species)) +
    geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
    geom_jitter(width = 0.1, show.legend = TRUE) +
    theme_minimal() +
    theme(
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      panel.border = element_blank())
  print(plt)
}

6.6 Comparison of samples from the 6th Time_point (6_Post-FMT2)

6.6.1 GIFTs Functional community

GIFTs_functions_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.352 0.0223
2 Hot_control 0.351 0.0276
3 Treatment   0.346 0.0255

6.6.1.1 GIFT test visualisation

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  select(c(1:21, 27)) %>%
  pivot_longer(-c(sample,type),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
      trait %in% GIFT_db$Code_function ~ GIFT_db$Function[match(trait, GIFT_db$Code_function)],
      TRUE ~ trait
    )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db$Function))) %>%
  ggplot(aes(x=value, y=type, group=type, fill=type, color=type)) +
    geom_boxplot() +
    scale_color_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Control","Hot control", "Treatment"),
          values=c("#76b183","#d57d2c", "#4477AA")) +
      scale_fill_manual(name="type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Control","Hot control", "Treatment"),
          values=c("#76b18350","#d57d2c50", "#4477AA50")) +
    facet_grid(trait ~ ., space="free", scales="free") +
              theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Metabolic capacity index")

6.6.2 GIFTs Domain community

GIFTs_domains_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.399 0.0171
2 Hot_control 0.386 0.0308
3 Treatment   0.392 0.0240

6.6.3 GIFTs Elements community

GIFTs_elements_community %>%
  rowMeans() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  group_by(type) %>%
  summarise(MCI = mean(value), sd = sd(value))
# A tibble: 3 × 3
  type          MCI     sd
  <chr>       <dbl>  <dbl>
1 Control     0.357 0.0215
2 Hot_control 0.346 0.0288
3 Treatment   0.350 0.0293
sample_metadata_TM6 <- sample_metadata%>% 
  filter(time_point == "6_Post-FMT2")%>% 
  filter(type != "Hot_control")

element_gift_TM6 <- GIFTs_elements_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(sample_metadata_TM6 %>% select(1, 7), by = "Tube_code")
# Find numeric columns
numeric_cols <- sapply(element_gift_TM6, is.numeric)

# Calculate column sums for numeric columns only
col_sums_numeric <- colSums(element_gift_TM6[, numeric_cols])

# Identify numeric columns with sums not equal to zero
nonzero_numeric_cols <- names(col_sums_numeric)[col_sums_numeric != 0]

# Remove numeric columns with sums not equal to zero
filtered_data <- element_gift_TM6[, !numeric_cols | colnames(element_gift_TM6) %in% nonzero_numeric_cols]
significant_elements_TM6 <- filtered_data %>%
  pivot_longer(-c(Tube_code,type), names_to = "trait", values_to = "value") %>%
  group_by(trait) %>%
  summarise(p_value = wilcox.test(value ~ type, exact=FALSE)$p.value) %>%
  mutate(p_adjust=p.adjust(p_value, method="BH")) %>%
  filter(p_value < 0.05)  %>% #take into account that p_value is used and not p_adjust
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(trait == Code_element))

element_gift_t <- element_gift_TM6  %>% 
  dplyr::select(-c(type))  %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "trait")

element_gift_filt <- subset(element_gift_t, trait %in% significant_elements_TM6$trait) %>% 
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Tube_code")%>% 
  left_join(., sample_metadata_TM6[c(1,7)], by = join_by(Tube_code == Tube_code))

element_gift_filt %>%
  dplyr::select(-Tube_code)%>%
  group_by(type)  %>%
  summarise(across(everything(), mean))%>%
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))

element_gift_names <- element_gift_filt%>%
  dplyr::select(-type)%>%
  t() %>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Elements")  %>%
  left_join(.,uniqueGIFT_db[c(1,3)],by = join_by(Elements == Code_element))%>%
  dplyr::select(-Elements)%>%
  dplyr::select(Function, everything())%>%
  t()%>%
  row_to_names(row_number = 1) %>%
  as.data.frame() %>%
  mutate_if(is.character, as.numeric)  %>%
  rownames_to_column(., "Tube_code")%>% 
  left_join(., sample_metadata_TM6[c(1,7)], by = join_by(Tube_code == Tube_code))
colNames <- names(element_gift_names)[2:20] #always check names(element_gift_names) first to now were your traits finish
for(i in colNames){
  plt <- ggplot(element_gift_names, aes(x=type, y=.data[[i]], color = type)) +
    geom_boxplot(alpha = 0.2, outlier.shape = NA, width = 0.3, show.legend = FALSE) +
    geom_jitter(width = 0.1, show.legend = TRUE) +
    theme_minimal() +
    theme(
      axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      panel.border = element_blank())
  print(plt)
}

6.7 Domain level

6.7.1 Comparison of samples from the 0 Time_point (0_Wild)

#Merge the functional domains with the metadata
merge_gift_wild<- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_wild, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Biosynthesis,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

#Degradation
p2 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Degradation,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.45, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

#Structure
p3 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Structure,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

#Overall
p4 <-merge_gift_wild %>%
  ggplot(aes(x=species,y=Overall,color=species,fill=species))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Species")

6.7.2 Comparison of samples from the 6th Time_point (6_Post-FMT2)

#Merge the functional domains with the metadata
merge_gift_TM6 <- GIFTs_domains_community %>% 
  as.data.frame() %>% 
  rownames_to_column(., "Tube_code") %>% 
  inner_join(., sample_metadata_TM6, by="Tube_code")
#Biosynthesis
p1 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Biosynthesis,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "Type")

#Degradation
p2 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Degradation,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")

#Structure
p3 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Structure,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 3, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")

#Overall
p4 <-merge_gift_TM6 %>%
  ggplot(aes(x=type,y=Overall,color=type,fill=type))+
  geom_jitter(width = 0.2, size = 1.5, show.legend = FALSE)+ 
  geom_boxplot(alpha=0.2,outlier.shape = NA, width = 0.5, show.legend = FALSE, coef=0)+
  stat_compare_means() +
  theme(axis.text.x = element_text(vjust = 0.5, size=10),
        axis.text.y = element_text(size=10),
        axis.title=element_text(size=12,face="bold"),
        axis.text = element_text(face="bold", size=18),
        panel.background = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
        legend.text = element_text(size=10),
        legend.title = element_text(size=12),
        legend.position="none",
        legend.key.size = unit(1, 'cm'),
        strip.text.x = element_text(size = 12, color = "black", face = "bold"))+
  labs( x = "type")